Development of knowledge-based ontology framework for diabetes patients in medical applications

Li Chen, Dongxin Lu, Sandeep Pirbhulal, Ali Hassan Sodhro, Zhenda Chen, Guixin Huang, Hongyan Wu

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Diabetes is a disease that affects an estimated 422 million adults, and the total costs of diagnosed diabetes have raised more than $245 billion. The main limitations of existing, ontology-based methods for diagnosing diabetes are that they not only have semantic inconsistencies, but also they have not provided a complete, clinical approach due to consideration of a few numbers of classes in their models. In this study, a knowledge-based ontology framework (KBOF) is developed for screening and treating diabetic patients. The proposed KBOF provides a complete semantic and clinical approach by adding more detailed analysis of patients based on a standard ontology. We have implemented the developed KBOF on Web Ontology Language (OWL), which is a semantic-web language; it enables us to create a knowledge-based representation of diabetic patients by applying different parameters. From the comparative analysis, we observed that the proposed KBOF is more feasible and accurate than traditional models for managing diabetes.

Original languageEnglish
Pages448-451
Number of pages4
DOIs
Publication statusPublished - 2018-Apr-26
Externally publishedYes
Event2nd International Conference on Biological Information and Biomedical Engineering, BIBE 2018 - Shanghai, China
Duration: 2018-Jul-062018-Jul-08

Conference

Conference2nd International Conference on Biological Information and Biomedical Engineering, BIBE 2018
Country/TerritoryChina
CityShanghai
Period18-07-0618-07-08

Swedish Standard Keywords

  • Computer and Information Sciences (102)

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